The images which are displayed on cathode ray tube (CRT) display devices such as traditional television monitors and other types of monitors are commonly rasterized images. A rasterized image is made up of a plurality of discrete lines. For example, an NTSC rasterized image contains 525 lines divided into two fields. This type of divided image is referred to as an interlaced image. Each of these fields are used alternately to produce an image on the display device. The display device uses a time varying voltage signal to produce each image line. Each such time varying voltage signal representing a line of a rasterized image will be referred to herein as an image line signal. The image line signals are received serially one after the other, and the display device produces each image line similarly one after the other. Once one image is produced, the process starts over again to produce the next interlaced image. The speed at which the individual image line signals are received and the speed at which the corresponding image lines are written onto the display device is fast enough that the viewer perceives only the overall image. The individual images themselves are each replaced by the succeeding image fast enough that the process appears continuous.
There are many situations in which it is desirable to detect a particular image in an incoming video signal. When recording a commercial television broadcast for example, it may be desirable to detect an advertisement and prevent the advertisement from being recorded along with desired programming. The ability to detect a particular image in an incoming stream of image signals may also be useful in monitoring security or surveillance video feeds. Changing images in the incoming stream of image signals indicates motion.
There have been a number of attempts to provide a reliable system for detecting advertisements in video signals. Prior detection systems fall into the following categories:
(1) systems which detect an audio and/or video fade as a precursor to an advertisement; PA1 (2) systems which sample the image signal frequently during each line of an image, quantizing the sample and comparing the quantized values to previously stored values; PA1 (3) systems which process image samples to isolate an object in the image, and then scan for the isolated object in the same lines of an image being tested; PA1 (4) systems which sample the image signals at multiple points between the start and end of an image and construct a series of digital values indicative of the image; PA1 (5) systems which use embedded information in the non-displayed image signals or associated audio signals to identify images or types of images; PA1 (6) systems which detect specific changes in image fields, possibly over long periods of time, by matching data samples from two distinct fields; and PA1 (7) systems which detect images by applying neural network recognition techniques to data obtained by sampling frequently over a line of an image.
There are number of problems associated with the prior methods used to detect an image in an incoming stream of image signals. Systems which rely on video and audio fading are not reliable because such fading may not occur between multiple advertisements or between advertisements and program segments. Systems which rely on embedded information in the incoming image signals are unreliable because they depend upon the cooperation of the entity transmitting the signal. Sampling techniques generally assume that the image signal is identical for an image used to produce a stored representation and the current incoming image. In fact, signal noise and horizontal shifting of the image signals caused by signal re-synchronization at local stations may result in significantly different sampled values for the same image. Furthermore, many of the current image detection techniques are expensive to implement relative to the cost of a television set or VCR. The prior art devices require high-speed circuitry to facilitate the required real-time comparison between the incoming image and stored images. The prior art systems also require large amounts of memory to store images to be detected.